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Title
CDH13 genotype-dependent association of high-molecular-weight adiponectin
with all-cause mortality: The J-SHIPP Study
Short title
Adiponectin and mortality
Authors
Eri Uetani MD, PhD;1) Yasuharu Tabara PhD;1)2) Ryuichi Kawamoto MD,
PhD;3) Hiroshi Onuma MD, PhD;4) Katsuhiko Kohara MD, PhD;1) Haruhiko
Osawa MD, PhD;4) and Tetsuro Miki MD, PhD1)
Affiliation
1)
2)
3)
4)
Correspondence
Department of Geriatric Medicine, Ehime University Graduate School of
Medicine, Toon, Japan
Center for Genomic Medicine, Kyoto University Graduate School of
Medicine, Kyoto, Japan
Department of Community Medicine, Ehime University Graduate School of
Medicine, Toon, Japan
Department of Molecular and Genetic Medicine, Ehime University Graduate
School of Medicine, Toon, Japan
Yasuharu Tabara,
Center for Genomic Medicine,
Kyoto University Graduate School of Medicine,
Shogoin-kawaramachi,
Sakyo-ku, Kyoto, 606-8507, Japan
Tel: +81-75-366-7407
Fax: +81-75-751-4167
E-mail: tabara@genome.med.kyoto-u.ac.jp
Word count
Abstract 250 words, body 2,136 words (introduction to discussion)
Two tables, two figures, 30 references
Diabetes Care
ABSTRACT
Objective: Despite its anti-inflammatory and anti-atherogenic effects, adiponectin is potentially
associated with adverse clinical outcomes, such as all-cause mortality. As plasma adiponectin
levels are strongly influenced by single nucleotide polymorphisms in the gene encoding
T-cadherin (CDH13), we conducted a longitudinal study to investigate the possible link
between the CDH13 genotype, plasma adiponectin levels, and all-cause mortality.
Research Design and Methods: This longitudinal study evaluated 2,020 Japanese subjects.
Baseline clinical parameters were obtained from subjects’ personal health records as evaluated
at annual medical check-ups. Plasma high-molecular-weight adiponectin (HMWA) levels were
measured by an ELISA assay, and genotyping was performed by a TaqMan probe assay.
Results: Mean follow-up duration was 6.5 years. Kaplan-Meier analysis showed that HMWA
levels were positively associated with mortality (p<0.001). HMWA levels were associated with
older age, lower body weight, lower plasma triglyceride and glucose levels, and higher plasma
HDL cholesterol. However, the Cox regression analysis showed that the positive association
between HMWA and all-cause mortality was independent of these covariates (hazard
ratio=1.92, p=0.006). The CDH13 rs4783244 genotype was strongly associated with baseline
HMWA levels (per-allele effect size =1.65 µg/ml, p<0.001). In a separate analysis by the
CDH13 genotype, the hazard ratio for all-cause mortality was linearly increased with the
number of G-alleles (p-value for HMWA*CDH13 genotype interaction=0.023).
Conclusions: Higher plasma HMWA level was an independent prognostic factor for all-cause
mortality in a general population. The CDH13 genotype may be a factor that affects not only the
plasma level of HMWA but also the prognostic significance of HMWA.
KEYWORDS
Adiponectin, all-cause mortality, CDH13 genotype
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Adiponectin is an adipocyte-derived secretory protein that has peripheral insulin-sensitizing
activity (1). Adiponectin also exerts systemic anti-inflammatory effects via suppression of the
growth and proliferation of macrophage progenitors, as well as the inflammation caused by
fully differentiated macrophages (1). Further, cross-sectional studies in various populations
have reported that a higher plasma adiponectin level is associated with a lower frequency of
type 2 diabetes (2), metabolic syndrome (3), and thinner carotid arterial wall thickness (4). The
results of these experimental studies and population-based cross-sectional association studies
indicate that adiponectin is a cardio-protective factor in humans.
In contrast, several longitudinal epidemiological studies have reported that a higher
plasma adiponectin level was associated with an increased incidence of cardiovascular disease
(5, 6) and mortality (7-12). The underlying mechanism of the inverse association between these
cross-sectional association studies and longitudinal prospective studies, the “adiponectin
paradox”, is unclear and prevents a complete understanding of the biological role of
adiponectin. A number of possibilities warrant investigation including reverse causality
stemming from a compensatory increase of adiponectin to subclinical cardiovascular disease
risks and adiponectin resistance (13).
Recent advances in DNA array technology have enabled the high-throughput analysis
of single-nucleotide polymorphisms (SNPs). Genome-wide association studies (GWAS) have
successfully identified SNPs of the CDH13 gene that have a strong association with plasma
adiponectin levels (14, 15). The CDH13 gene encodes T-cadherin, a receptor for hexameric and
high-molecular-weight forms of adiponectin (HMWA) (16). Although earlier studies (17, 18)
and recent GWAS in European subjects (19, 20) reported a strong susceptibility of SNPs in the
ADIPOQ gene that encodes adiponectin, GWAS of adiponectin in East-Asian subjects, namely
Korean (15) and Japanese (21), identified SNPs in the CDH13 locus, such as rs4783244.
Because plasma adiponectin levels in subjects homozygous for the allele that with an
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association with higher adiponectin levels (affected allele) were approximately 1.5 times higher
than those homozygous for the allele without (unaffected allele) (21), genetic variation might
be partially involved in the adiponectin paradox.
To clarify our hypothesis, we analyzed the possible interaction of the CDH13 genotype
on the association of plasma levels of HMWA and all-cause mortality in Japanese middle-aged
to elderly community residents.
RESEARCH DESIGN AND METHODS
Study subjects
This longitudinal study enrolled community-dwelling Japanese subjects living in Nomura
Town, Ehime Prefecture, a mostly rural town with 11,000 inhabitants. Study subjects were
recruited through a community-based annual medical check-up process in 2002 as part of the
Shimanami Health Promoting Program (J-SHIPP) (3, 22, 23), which was a longitudinal
epidemiological study evaluating factors relating to cardiovascular disease (CVD), dementia,
and death. Among a total of 2,721 residents who agreed to participate in the J-SHIPP study and
completed baseline clinical measurements, 2,020 persons for whom an overnight fasting
plasma sample was provided (> 11 h) for HMWA measurement (n=2,084) and CDH13
genotype was determined (n=2,042), and whom mortality during this study period could be
followed-up were enrolled in this analysis. All study procedures were approved by the Ethics
Committee of Ehime University Graduate School of Medicine, and all subjects provided signed
informed consent prior to participation.
Baseline measurements
Baseline clinical characteristics evaluated at medical check-ups, including anthropometric
measurements, blood pressure (BP) and basic plasma markers, were obtained from personal
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health records. Additional characteristics, including medication and history of CVD, were
obtained by individual interviews using a structured questionnaire. Hypertension was defined
as any or all of the following: systolic blood pressure (SBP) equal to or greater than 140 mmHg,
diastolic blood pressure (DBP) equal to or greater than 90 mmHg, and current use of
antihypertensive medication. Subjects with a fasting blood glucose level of more than 126
mg/dl or currently using antihyperglycemic medication were defined as having type 2 diabetes
mellitus. Renal function was evaluated based on the estimated glomerular filtration rate (eGFR),
which was calculated from the plasma creatinine values using the following formula
specifically developed by the Japanese Society of Nephrology for the estimation of the GFR in
Japanese: 194 x creatinine-1.094 x age-0.287 x 0.739 (if female).
Follow-up of all-cause mortality
Vital status was ascertained until the 30th of November 2009 by checking a residential
registration certification, which is under local government control. Since the registration of a
person’s death is obligatory under the Family Registration Law in Japan, the local
government’s vital statistics database enabled the follow-up of all-cause mortality. Further, as
the notification of a change of address is obligatory under the residential registration certificate
system, we were informed of subjects who moved outside of the cohort area and treated them as
a censored case.
Measurement of plasma HMWA levels
Plasma samples were obtained from each subject after a period of overnight fasting of at least
11 h. Samples were immediately frozen and stored at -80 °C until analysis. Plasma
concentrations of HMWA were determined using an ELISA assay (Fujirebio Inc., Shinjuku-ku,
Tokyo, Japan) (3, 24), which consisted of HMWA-specific capture antibody (IH7) and
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HMWA-specific horseradish peroxidase-conjugated detection antibody (POD-IH7). The
specificity of this monoclonal antibody has been described elsewhere (25). Standardized
HMWA concentrations were determined by human HMWA purified by gelatin-cellulofine
column chromatography. Intra- and inter-assay coefficient variations of the adiponectin assay
were 4.4% and 9.7%, respectively.
Genotyping
DNA was extracted from peripheral blood using a QIAamp DNA blood kit (Qiagen GmbH,
Hilden, North Rhine-Westphalia, Germany). The SNP rs4783244 in the CDH13 gene was
genotyped by a TaqMan probe assay using commercially available primers and probes
purchased from the Assay-on-Demand system (Life Technologies Corporation, Carlsbad,
California, USA). Fluorescence of PCR products was measured using an ABI PRISM 7900HT
sequence detector (Life Technologies, Minato-ku, Tokyo, Japan).
Statistical analysis
Differences in the numeric variables of baseline data were assessed by analysis of variance, and
differences in frequency were assessed by a chi-square test. Overall survival ratio was
calculated by Kaplan-Meier analysis. Covariate-adjusted hazard ratio (HR) for total mortality
was analyzed by Cox proportional hazards regression analysis. Combined HR was calculated
under a random effects model. A p-value less than 0.05 was considered statistically significant.
RESULTS
Baseline clinical characteristics of study subjects are shown in Table 1. Mean HMWA level in
female subjects (7.62 ± 4.56 µg/ml) was nearly double that in male subjects (4.12 ± 2.93 µg/ml,
p<0.001). Subjects were therefore divided by HMWA quartile within each sex and then
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combined to avoid potential sex differences (Table 1). Table 1 also summarizes the differences
in clinical parameters among HMWA quartiles. Age was strongly and positively associated
with HMWA quartile, while BMI showed a significant inverse association. Plasma levels of
HDL cholesterol, triglyceride, and glucose were also significantly different among HMWA
quartiles. In a simple comparison, systolic blood pressure and eGFR were significantly
associated with HMWA quartile; however, these associations disappeared after adjustment for
age and BMI.
Mean follow-up duration was 6.5 years, during which time 84 subjects died. Figure 1A
shows the Kaplan-Meier plot for all-cause mortality by HMWA quartile, with the highest
quartile being strongly associated with all-cause mortality. To clarify whether the prognostic
significance of HMWA was independent of possible covariates, Cox regression analysis was
performed by a forward covariate selection method. Table 2 summarizes the hazard ratio (HR)
of the 4th quartile of HMWA, as well as 10 µg/ml increases in HMWA, on total mortality. In
any model adjusted for relevant clinical parameters (Table 2, Model 1 to 3), higher plasma
HMWA levels were independently associated with total mortality.
The G-allele of the CDH13 rs4783244 genotype was strongly associated with baseline
plasma HMWA level (Figure 2). Although obese subjects, defined as having a body mass index
greater than 25 kg/m2, showed significantly lower HMWA levels in both sexes (male: obese
3.25±2.22, non-obese 4.49±3.12, p<0.001; female: 6.37±3.68, 8.18±4.86, p<0.001), the
association between CDH13 genotype and HMWA was independent of obese status (Figure 2).
Per-allele effect size of the genotype calculated by a regression analysis adjusted for age, sex
and BMI was 1.65 µg/ml (p<0.001).
Nevertheless, the prognostic significance of HMWA on total mortality was
independent of the CDH13 genotype (Table 2, Model 4). In a separate analysis by the genotype,
however, HR for all-cause mortality was linearly increased with the number of G-alleles
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(Figure 1B), and the association between HMWA and poor prognosis was seen only in the GG
genotype carries. The term of interaction between the CHD13 genotype and plasma HMWA
level was identified as a significant factor for all-cause mortality (p=0.023) in the Cox
regression analysis adjusted for the same covariates as adjusted in Model 3 of Table 2. These
results indicate that the poor prognostic significance of HMWA was genotype-dependent.
CONCLUSIONS
In this longitudinal study in a general population, we observed that higher plasma HMWA
levels were significantly associated with all-cause mortality. Further, the association between
HMWA and mortality showed a high degree of variation among CDH13 rs4783244 genotypes.
To our knowledge, this is the first study showing a genotype-dependent association of HMWA
with all-cause mortality.
Our results confirmed the increased risk of all-cause mortality in subjects with a higher
baseline HMWA level. Several previous studies reported a similar relationship in general
populations (5, 8-10), as well as in patients with type 2 diabetes (7), coronary artery disease (6,
11), and chronic heart failure (12). Higher plasma HMWA in a general population is a complex
phenotype which may represent a clustering of sub-clinical risk statuses, namely reduced renal
and cardiac function, obesity, insulin resistance and metabolic disorders, atherosclerosis,
systemic inflammation, weight loss in elderly, and anemia. However, these studies were
conducted mainly in European subjects (7, 9-12) or in European and African American subjects
(5, 6, 8), with no positive results being reported from East-Asian subjects. In contrast, it has
been reported that the Japanese population have distinct anthropometric characteristics,
including a lower frequency of obesity compared with developed Western countries (26), and
considerably lower plasma adiponectin levels (27), even in comparison with Japanese
Americans living in Hawaii (28). In populations with lower HMWA levels, our results
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demonstrate the cross-validity of the prognostic significance of HMWA.
We also found an inverse association between HMWA and all-cause mortality between
CDH13 TT and CDH13 GG genotype carriers, with the HR of the CDH13 TT genotype failing
to reach statistical significance, presumably due to the smaller sub-sample size. Although the
CDH13 gene is known to encode T-cadherin, a receptor for HMWA, the functionality of each
CDH13 rs4783244 genotype or other unidentified responsible SNPs in linkage disequilibrium
have not been clarified. However, T-cadherin knockout mice were reported to show
dramatically increased plasma adiponectin levels due to impaired linkage between adiponectin
and target cells (29). It is therefore possible that the G-allele of the CDH13 genotype causes
secondary hyper-adiponectinemina by altering the amount or functionality of T-cadherin. This
genetically dependent adiponectin resistance and consequent reduction in adiponectin’s
anti-inflammatory and anti-atherogenic effects may explain the increased mortality ratio in
subjects with higher HMWA levels. Because differences in HMWA levels for each CDH13
rs4783244 genotype were similar between non-obese and obese subjects (Figure 2), the
production of adiponectin and formation of high-molecular-weight isoforms might not be a
factor in the genotype-specific mortality ratio.
According to the HapMap database (http://hapmap.ncbi.nlm.nih.gov/), frequency of
the CDH13 rs4783244 genotype is similar across all ethnic groups. However, GWAS in
European subjects (19-20) identified only the ADIPOQ gene as susceptible loci for plasma
adiponectin levels. Although more large-scale GWAS might identify the CDH13 gene as
another susceptible loci, it is probable that genetic polymorphisms in the ADIPOQ gene have a
larger effect than those in the CDH13 genotype in European populations regardless of the type
of adiponectin complex (30). Because the ADIPOQ gene encodes adiponectin, our “genetic
adiponectin resistance” hypothesis involving the CDH13 gene as a means of explanation might
not be easily extrapolated to other populations. However, our results provide novel supporting
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evidence that adiponectin resistance is a key mechanism of the adiponectin paradox, which is
lacking in other proposed mechanisms such as renal dysfunction and decreased adiponectin
clearance, weight loss or sarcopenia in the elderly, and compensatory rise in adiponectin due to
subclinical risks (reverse causality) (13). Our present results also excluded the possibility of
confounding effects of decreased renal function.
Several limitations of this study warrant mention. First, we could not evaluate a
cause-specific mortality, particularly cardiovascular mortality. Results of long-term analysis for
cardiovascular mortality would strengthen our findings. Second, we did not analyze other
molecular isoforms of adiponectin. As T-cadherin is a receptor for HMWA and the HMW
isoform is considered an active form, further investigations regarding isoform specificity will
be necessary. Third, this study contained approximately 2,000 study subjects. Replication
studies, particularly in East-Asian populations, with a larger sample size are therefore needed to
confirm our present findings.
In summary, our longitudinal study demonstrated that the prognostic significance of
HMWA is genotype-specific. Our findings may provide the insight necessary to help solve the
adiponectin paradox, and further highlight the importance of genetic factors in understanding
the contribution of adiponectin levels to adverse clinical outcomes.
ACKNOWLEDGEMENTS
The authors have no conflicts of interest to disclose.
Contribution of each author: E.U conducted research data acquisition, statistical
analysis, and manuscript writing; Y. T conducted research data acquisition, statistical analysis,
and manuscript writing; R.Y and H.On conducted research data acquisition; K.K and H. Os
conducted research data acquisition, and contributed to the Conclusions section; and T.M
supervised the study. Y.T is the guarantor of this work and, as such, had full access to all the
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data in the study and takes responsibility for the integrity of the data and the accuracy of the
data analysis.
This study was supported by a Grant-in-Aid for Scientific Research from The Ministry
of Education, Culture, Sports, Science and Technology of Japan; a Science and Technology
Incubation Program in Advanced Regions from the Japan Science and Technology Agency; a
Grant-in-Aid for Scientific Research from the Japan Arteriosclerosis Prevention Fund; and a
Research Promotion Award from Ehime University.
We thank Nicholas Crabb and Guy Harris of DMC Corporation for help in the
preparation of this manuscript.
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Blagieva R, Boerwinkle E, Bonnycastle LL, Boström KB, Bravenboer B, Bumpstead S,
Burtt NP, Charpentier G, Chines PS, Cornelis M, Crawford G, Doney AS, Elliott KS, Elliott
AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S,
Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson
PR, Jørgensen T, Kao WH, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A,
Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N,
Nilsson P, Owen KR, Payne F, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann
W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields
BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ,
Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van
Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN,
Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman
A, Hunter DJ, Hveem K, Laakso M, Morris AD, Palmer CN, Rudan I, Sijbrands E, Stein
LD, Tuomilehto J, Uitterlinden A, Walker M, Watanabe RM, Abecasis GR, Boehm BO,
Campbell H, Daly MJ, Hattersley AT, Pedersen O, Barroso I, Groop L, Sladek R,
Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D,
Boehnke M, McCarthy MI, Soranzo N, Wheeler E, Glazer NL, Bouatia-Naji N, Mägi R,
Randall J, Elliott P, Rybin D, Dehghan A, Hottenga JJ, Song K, Goel A, Lajunen T, Doney
A, Cavalcanti-Proença C, Kumari M, Timpson NJ, Zabena C, Ingelsson E, An P, O'Connell
J, Luan J, Elliott A, McCarroll SA, Roccasecca RM, Pattou F, Sethupathy P, Ariyurek Y,
Barter P, Beilby JP, Ben-Shlomo Y, Bergmann S, Bochud M, Bonnefond A, Borch-Johnsen
K, Böttcher Y, Brunner E, Bumpstead SJ, Chen YD, Chines P, Clarke R, Coin LJ, Cooper
MN, Crisponi L, Day IN, de Geus EJ, Delplanque J, Fedson AC, Fischer-Rosinsky A,
Forouhi NG, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Grundy S, Gwilliam R,
Hallmans G, Hammond N, Han X, Hartikainen AL, Hayward C, Heath SC, Hercberg S,
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Diabetes Care
Hillman DR, Hingorani AD, Hui J, Hung J, Kaakinen M, Kaprio J, Kesaniemi YA,
Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le
Bacquer O, Lecoeur C, Li Y, Mahley R, Mangino M, Martínez-Larrad MT, McAteer JB,
McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Mukherjee S, Naitza S,
Neville MJ, Orrù M, Pakyz R, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL,
Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Rayner
NW, Rice K, Ripatti S, Rivadeneira F, Rolandsson O, Sandbaek A, Sandhu M, Sanna S,
Sayer AA, Scheet P, Seedorf U, Sharp SJ, Shields B, Sigurðsson G, Sijbrands EJ, Silveira A,
Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tönjes A,
Uitterlinden AG, van Dijk KW, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber
G, Wagner PJ, Walley A, Ward KL, Watkins H, Wild SH, Willemsen G, Witteman JC,
Yarnell JW, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC; DIAGRAM
Consortium; GIANT Consortium; Global B Pgen Consortium, Borecki IB, Meneton P,
Magnusson PK, Nathan DM, Williams GH, Silander K, Bornstein SR, Schwarz P, Spranger
J, Karpe F, Shuldiner AR, Cooper C, Serrano-Ríos M, Lind L, Palmer LJ, Hu FB 1st, Franks
PW, Ebrahim S, Marmot M, Kao WH, Pramstaller PP, Wright AF, Stumvoll M, Hamsten A;
Procardis Consortium, Buchanan TA, Valle TT, Rotter JI, Penninx BW, Boomsma DI, Cao
A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Peltonen L, Mooser V,
Sladek R; MAGIC investigators; GLGC Consortium, Musunuru K, Smith AV, Edmondson
AC, Stylianou IM, Koseki M, Pirruccello JP, Chasman DI, Johansen CT, Fouchier SW,
Peloso GM, Barbalic M, Ricketts SL, Bis JC, Feitosa MF, Orho-Melander M, Melander O,
Li X, Li M, Cho YS, Go MJ, Kim YJ, Lee JY, Park T, Kim K, Sim X, Ong RT,
Croteau-Chonka DC, Lange LA, Smith JD, Ziegler A, Zhang W, Zee RY, Whitfield JB,
Thompson JR, Surakka I, Spector TD, Smit JH, Sinisalo J, Scott J, Saharinen J, Sabatti C,
Rose LM, Roberts R, Rieder M, Parker AN, Pare G, O'Donnell CJ, Nieminen MS,
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Nickerson DA, Montgomery GW, McArdle W, Masson D, Martin NG, Marroni F, Lucas
G, Luben R, Lokki ML, Lettre G, Launer LJ, Lakatta EG, Laaksonen R, Kyvik KO, König
IR, Khaw KT, Kaplan LM, Johansson Å, Janssens AC, Igl W, Hovingh GK, Hengstenberg
C, Havulinna AS, Hastie ND, Harris TB, Haritunians T, Hall AS, Groop LC, Gonzalez E,
Freimer NB, Erdmann J, Ejebe KG, Döring A, Dominiczak AF, Demissie S, Deloukas P, de
Faire U, Crawford G, Chen YD, Caulfield MJ, Boekholdt SM, Assimes TL, Quertermous T,
Seielstad M, Wong TY, Tai ES, Feranil AB, Kuzawa CW, Taylor HA Jr, Gabriel SB, Holm
H, Gudnason V, Krauss RM, Ordovas JM, Munroe PB, Kooner JS, Tall AR, Hegele RA,
Kastelein JJ, Schadt EE, Strachan DP, Reilly MP, Samani NJ, Schunkert H, Cupples LA,
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influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891
individuals. PLoS Genet. 2012;8:e1002607.
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Table 1. Clinical parameters of HMWA quartiles
Total subjects
HMW Adiponectin quartile
p-value
Q1
Q2
Q3
Q4
ANOVA
Trend
(2,020)
(501)
(507)
(507)
(505)
(crude)
(adjusted)
HMW adiponectin (g/ml)
6.09 ± 4.33
2.34±1.07
4.21±1.51
6.36±2.05
11.43±4.60
Age (years)
62.4 ± 12.3
57.9 ± 11.9
60.9 ± 12.4
63.3 ± 11.7
67.6 ± 11.0
<0.001
Sex (male%)
44.2
44.3
43.8
43.4
44.2
0.998
BMI (kg/m )
23.5 ± 3.2
24.6 ± 3.2
23.8 ± 3.2
23.3 ± 2.9
22.2 ± 3.0
<0.001
Systolic BP (mmHg)
139 ± 22
140 ± 22
136 ± 21
139 ± 23
142 ± 22
0.001
0.995
Total cholesterol (mg/dl)
203 ± 35
205 ± 36
204 ± 35
201 ± 33
203 ± 34
0.302
0.093
HDL cholesterol (mg/dl)
62 ± 16
56 ± 14
60 ± 14
63 ± 15
68 ± 16
<0.001
<0.001
Triglyceride (mg/dl)
113 ± 74
139 ± 92
115 ± 69
107 ± 64
91 ± 58
<0.001
<0.001
eGFR (mL/min/1.73m )
80 ± 17
82 ± 18
80 ± 17
81 ± 17
78 ± 17
<0.001
0.266
Glucose (mg/dl)
98 ± 21
102 ± 24
98 ± 23
98 ± 22
95 ± 15
<0.001
<0.001
Antihyperglycemic medication (%)
3.4
3.8
2.8
4.1
3.0
0.575
Type 2 diabetes (%)
7.4
10.6
6.9
6.7
5.4
0.011
CDH13 genotype (G-allele %)
65.8
51.7
65.3
68.3
77.9
<0.001
2
2
Values are mean ± standard deviation. Quartile of HMWA was calculated within each sex and then combined to avoid potential sex differences.
Statistical significance in numeric variables was assessed by analysis of variance (ANOVA), while differences in frequency were assessed by a
chi-squared test. Trends in numeric variables among the HMWA quartile was assessed by regression analysis adjusted for age and BMI. The
estimated glomerular filtration rate (eGFR) was calculated from plasma creatinine values using the following formula: 194 x creatinine-1.094 x
age-0.287 x 0.739 (if female). Type 2 diabetes was defined as a fasting plasma glucose level greater than 126 mg/dl, or the current use of medication
to lower blood glucose levels. G-allele frequency of the CDH13 rs4783244 genotype is shown in the Table. BMI, body mass index; BP, blood
pressure; HMWA, high-molecular-weight adiponectin.
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Table 2. Hazard ratio of plasma HMWA levels on total mortality
HMW Adiponectin
Model
Adjusted factors
10g/ml
4th quartile
HR (95% C.I.)
p-value
HR (95% C.I.)
p-value
Model 1
Age, sex
2.03 (1.34-2.96)
0.001
1.78 (1.19-2.68)
0.006
Model 2
Age, sex, BMI
1.85 (1.20-2.76)
0.006
1.63 (1.08-2.48)
0.021
1.91 (1.20-2.96)
0.007
1.59 (1.03-2.46)
0.035
1.83 (1.12-2.91)
0.017
1.51 (0.97-2.37)
0.068
Age, sex, BMI, SBP, TC, HDLC, glucose, CVD, eGFR,
Model 3
current smoking, current medication
Age, sex BMI, SBP, TC, HDLC, glucose, CVD, eGFR,
Model 4
current smoking, current medication, CDH13 genotype
Hazard ratio (HR) was calculated by a cox regression analysis. CVD indicates previous history of cardiovascular diseases (namely stroke and
myocardial infarction). Medication includes antihypertensive drugs and antihyperglycemic drugs. BMI, body mass index; SBP, systolic blood
pressure; TC, total cholesterol; HDLC, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate.
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FIGURE LEGENDS
Figure 1. HMWA and all-cause mortality
A: Kaplan-Meier plot of survival by HMWA quartile. Subjects were divided by HMWA quartile
within each sex and then combined to avoid potential sex differences. B: Hazard ratio of
HMWA with total mortality and CDH13 rs4783244 genotype. Hazard ratio of HMWA
(continuous variable, per 10 µg/ml) for each CDH13 rs4783244 genotype was calculated by a
Cox regression analysis adjusted for age and sex. Diamond represents the combined odds ratio
calculated by a random effects model.
Figure 2. Mean HMWA level and CDH13 rs4783244 genotype
Values are mean ± standard deviation. Statistical significance was assessed by analysis of
variance (ANOVA). Number of subjects for each genotype is shown in the column. Obesity was
defined as a body mass index greater than 25 kg/m2.
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Diabetes Care
Figure 1
209x148mm (300 x 300 DPI)
Diabetes Care
Figure 2
209x148mm (300 x 300 DPI)
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